Flow cytometer apparatus for three dimensional difraction imaging and related methods

ABSTRACT

A flow cytometer assembly includes a fluid controller configured to form a hydrodynamically focused flow stream including an outer sheath fluid and an inner core fluid. A coherent light source is configured to illuminate a particle in the inner core fluid. A detector is configured to detect a spatially coherent distribution of elastically scattered light from the particle excited by the coherent light source. An analyzing module configured to extract a three-dimensional morphology parameter of the particle from a spatially coherent distribution of the elastically scattered light.

RELATED APPLICATION

This application claims priority to U.S. Provisional Application Ser.No. 61/060,993, filed Jun. 12, 2008, the disclosure of which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to flow cytometers, and in particular toflow cytometers for detection of three dimensional morphology parametersand imaging.

BACKGROUND

Flow cytometers are used in life science research for quantitativeassays of large populations of biological cells and particles. A beam oflight (typically of a single wavelength) is directed onto ahydrodynamically focused stream of fluid. The fluid stream typicallyincludes a fluid carrier or “sheath” and a core fluid including aplurality of particles. The fluid stream generally permits one particleto pass through the light beam at a time. A number of detectors can beaimed at the point where the stream passes through the light beam. Forexample, a detector can be positioned in line with the light beam todetect forward scatter and one or more detectors can be positionedperpendicular to the light beam to detect side scatter. The particlescan contain fluorescent components, and one or more fluorescentdetectors can be used to detect a resulting fluorescence signal. Eachsuspended particle passing through the light beam scatters the light insome way and/or a fluorescent component on the particle may fluorescelight, e.g., at a lower frequency than the light source. Scattered andfluorescent light can be detected and analyzed.

The signals detected from a flow cytometer can be used to characterizethe physical and/or chemical structure of the particles. For example,the forward scattered light can be correlated with a cell volume, andthe side scattered light may be correlated with the shape or other innerfeatures of the particle. The scattered and/or fluorescence signals areacquired by detectors that allow fast signal acquisition (e.g.,thousands of cells per second) and rapid data analysis for a large cellpopulation. For example, the cells can be classified in amulti-dimensional feature space defined by various fluorescence signals,forward scatter signals, and/or side scatter signals.

More recently, imaging flow cytometers are available in which a CCDcamera is used to record bright-field, dark field, and fluorescentimages. These imaging flow cytometers use microscope techniques toacquire two dimensional images from each interrogated cell for analysisof features at a rate of up to about 100 cells per second. However,these techniques rely on conventional fluorescence or bright-fieldmicroscopy in which the resulting images are non-diffractional andinherently two dimensional replicas of the three dimensional cellstructure (with the third dimension being compressed into a “focaldepth”). Although these two dimensional images can be analyzed withpattern recognition algorithms, automated analysis with existing patternrecognition algorithms is complex, labor intensive, and challenging atleast because of the two dimensional nature of the image.

Confocal imaging techniques have been used in non-flow applications toacquire multiple two dimensional non-diffraction images of very shortfocal depth and stack them along the third dimension to provide a threedimensional construction. However, this technique typically requiresmultiple images and, therefore, these confocal imaging techniques aregenerally not compatible with an imaging flow cytometer in which theparticles are moving relatively rapidly.

In addition, the high flow speeds and poor signal to noise ratios inconventional flow cytometers may limit the amount of information thatcan be extracted from the scattering and/or fluorescence signals.

SUMMARY OF EMBODIMENTS OF THE INVENTION

According to embodiments of the present invention, a flow cytometerassembly includes a fluid controller configured to form ahydrodynamically focused flow stream including an outer sheath fluid andan inner core fluid. A coherent light source is configured to illuminatea particle in the inner core fluid. A detector is configured to detect aspatially coherent distribution of elastically scattered light from theparticle excited by the coherent light source. An analyzing moduleconfigured to extract a three-dimensional morphology parameter of theparticle from a spatially coherent distribution of the elasticallyscattered light.

According to some embodiments of the invention, the detector is furtherconfigured to provide diffraction image data of the particle comprisingthe spatially coherent distribution of the elastically scattered light.

In particular embodiments, a non-coherent light source is configured toilluminate the particle and a detector is configured to detectnon-coherent image data comprising bright-field and/or dark-field and/orfluorescence signals from the particle excited by the non-coherent lightsource. The analyzing module can be configured to combine thediffraction image data and the non-coherent image data for a particle inthe core fluid.

In some embodiments, the analyzing module is configured to classify theparticles based on the coherence distribution of the elasticallyscattered light. The analyzing module can be configured to extract avolume of a structure of the particle based on the diffraction imagedata, such as a volume of the cytoplasm and/or nucleus and/ormitochondrion in a biological cell.

In some embodiments, the fluid controller is configured to form alaminar flow stream. The fluid controller can include a flow cell havingan index of refraction that is substantially similar to an index ofrefraction of the fluid sheath. The flow cell can have at least onegenerally planar side.

In some embodiments, the detector is configured to detect lightscattered within an angle range centered at an angle offset from adirection of light propagation from the coherent light source, such asat about 90 degrees.

In some embodiments, the three-dimensional morphology parameters areextracted based on a database of calculated diffraction image dataand/or experimentally determined cell structures.

According to further embodiments of the present invention, methods ofanalyzing particles in a flow cytometer to determine three-dimensionalmorphology parameters include forming a hydrodynamically focused flowstream including an outer sheath fluid and an inner core fluid. Aparticle in the inner core fluid is illuminated with a coherent lightsource. Elastically scattered light is detected from the particle thatis excited by the coherent light source. A three-dimensional morphologyparameter of the particle is extracted from a spatially coherentdistribution of the elastically scattered light.

According to further embodiments of the present invention, computerprogram products for analyzing particles in a flow cytometer todetermine three-dimensional morphology parameters are provided. The flowcytometer has a hydrodynamically focused flow stream including an outersheath fluid and an inner core fluid, a coherent light source configuredto illuminate a particle, and a detector for detecting a coherentdistribution of elastically scattered light from the particle excited bythe coherent light source. The computer program product includes acomputer usable storage medium having computer-readable program codeembodied in the medium. The computer-readable program code is configuredto receive diffraction image data comprising a spatially coherentdistribution of elastically scattered light from the flow cytometer.Computer-readable program code is configured to extractthree-dimensional morphology parameters of the particle from thespatially coherent distribution of the elastically scattered light.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention and,together with the description, serve to explain principles of theinvention.

FIG. 1 is a schematic diagram of a cytometer assembly according toembodiments of the present invention.

FIG. 2 is a schematic diagram of a cytometer assembly according to otherembodiments of the present invention.

FIG. 3 is a block diagram of a cytometer assembly including diffractionand non-diffraction imaging modules and featureextraction/classification modules according to embodiments of thepresent invention.

FIG. 4A is a schematic diagram of a cytometer assembly according toadditional embodiments of the present invention.

FIG. 4B is a schematic diagram of a cytometer assembly according toadditional embodiments of the present invention.

FIG. 5 is a schematic diagram of a cytometer assembly according toembodiments of the present invention.

FIG. 6 a is a digital image of a fluid control unit according toembodiments of the present invention.

FIG. 6 b is a schematic diagram of a cytometer assembly according toembodiments of the present invention.

FIG. 6 c is a digital image of a cytometer assembly according toembodiments of the present invention.

FIG. 6 d is a schematic diagram of the cytometer assembly of FIG. 6 c.

FIG. 7 is a schematic diagram of the incident field and scattering fieldfor a particle according to embodiments of the present invention.

FIG. 8 a is a diffraction image from a 25 μm microsphere flown in thecore fluid excited by a 633 m laser.

FIG. 8 b is a Mie theory-calculated diffraction image with horizontalaxis as θ_(s) and vertical as φ_(s) between 70° and 110° and noadjustable parameters.

FIG. 9 is a schematic representation of the incident and scatteredwavefronts by a biological cell with inhomogeneous distribution ofrefraction index, where nu=nucleus, m=mitochondria, g=Golgi apparatus,λ=wavelength, and 2a=size.

FIG. 10 is a table including the three dimensional structural parametersof seven NALM-6 cells.

FIG. 11 is a graph of 9 NALM-6 cells distributed in the 2D featuresubspaces defined by the light scatter of element S11 at θ_(s)=0° versusits integrated value in different angular range of θ_(s) as sidescatters.

FIG. 12 is a schematic diagram of a cytometer assembly according to someembodiments of the present invention.

FIGS. 13A-13C are bright-field digital images acquired with theobjective positioned at x=0 of a sphere of d=9.6 μm (FIG. 13A), a sphereof d=25 μm (FIG. 13B) and a B16/GPR4 cell with Bar=20 μm (FIG. 13C).

FIG. 14 illustrates digital diffraction images and one bright-fieldimage acquired with non-coherent white light (third row, first column)of a polystyrene sphere of 25 μm in diameter (embedded in gel). Thediffraction images were acquired with a laser beam of λ=532 nm inwavelength and the objective at different x positions. From left toright, first row: x=0 μm, 100 μm, 200 μm; second row: x=300 μm, 400 μm,500 μm; third row: x=0 μm (bright field image), −100 μm, −200 μm; fourthrow: x=−300 μm, −400 μm, −500 μm.

FIG. 15 illustrates digital diffraction images of a polystyrene sphereof 9.6 μm in diameter in the left column and two melanoma cells that areembedded in gel: B16/vector (cell #1) in the middle column and B16/GPR4(cell #3) in the right column. The first row is imaged at x=200 μm, andthe second row is imaged at x=−200 μm.

FIG. 16 illustrates digital projection images calculated fromangle-resolved scattered light distribution by the Mie theory with Θ=24°for images in the left column and Θ=16° in the middle column. The imagesin the right column are measured diffraction images of the spheresembedded in gel with x=200 μm with a diameter of 25 μm in the first rowand 9.6 μm in the second row.

FIGS. 17A-17B are graphs illustrating scatter plots of side scatterchannel (SSC) versus forward scatter channel (FSC) obtained from 10,000B16F10 cells fpr B16/GPR4 (FIG. 17A) and B16/vector (FIG. 17B).

FIG. 18 is an image of two cross-sectional views of thethree-dimensional structure of a B16/GPR4 cell.

FIG. 19 illustrates digital images of 9.6 μm diameter spheres in 532 nmlight with a flow speed of between about 1.6 mm/s and 1.8 mm/s and anexposure rate of 50 μs.

FIG. 20 illustrates digital images of 9.6 μm diameter spheres in 532 nmlight with a flow speed of about 12 mm/s and an exposure rate of 50 μs.

FIG. 21A is a digital image of a 5.2 μm diameter spheres with a flowspeed of between about 4.7 mm/s.

FIG. 21B is a digital image of a 9.6 μm diameter sphere with a flowspeed of about 12 mm/s.

FIG. 21C is a digital image of a 25 μm diameter sphere with a flow speedof about 7 mm/s.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention now will be described hereinafter with referenceto the accompanying drawings and examples, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

Like numbers refer to like elements throughout. In the figures, thethickness of certain lines, layers, components, elements or features maybe exaggerated for clarity. Broken lines illustrate optional features oroperations unless specified otherwise.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. As used herein, phrases such as “between X and Y” and“between about X and Y” should be interpreted to include X and Y. Asused herein, phrases such as “between about X and Y” mean “between aboutX and about Y.” As used herein, phrases such as “from about X to Y” mean“from about X to about Y.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

It will be understood that when an element is referred to as being “on,”“attached” to, “connected” to, “coupled” with, “contacting,” etc.,another element, it can be directly on, attached to, connected to,coupled with or contacting the other element or intervening elements mayalso be present. In contrast, when an element is referred to as being,for example, “directly on,” “directly attached” to, “directly connected”to, “directly coupled” with or “directly contacting” another element,there are no intervening elements present. It will also be appreciatedby those of skill in the art that references to a structure or featurethat is disposed “adjacent” another feature may have portions thatoverlap or underlie the adjacent feature.

Spatially relative terms, such as “under,” “below,” “lower,” “over,”“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if the device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of “over” and “under.” The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly,” “downwardly,” “vertical,” “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

It will be understood that, although the terms “first,” “second,” etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. Thus, a “first” element discussed below couldalso be termed a “second” element without departing from the teachingsof the present invention. The sequence of operations (or steps) is notlimited to the order presented in the claims or figures unlessspecifically indicated otherwise.

The present invention is described below with reference to blockdiagrams and/or flowchart illustrations of methods, apparatus (systems)and/or computer program products according to embodiments of theinvention. It is understood that each block of the block diagrams and/orflowchart illustrations, and combinations of blocks in the blockdiagrams and/or flowchart illustrations, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, and/or other programmable data processing apparatus to producea machine, such that the instructions, which execute via the processorof the computer and/or other programmable data processing apparatus,create means for implementing the functions/acts specified in the blockdiagrams and/or flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instructions whichimplement the function/act specified in the block diagrams and/orflowchart block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe block diagrams and/or flowchart block or blocks.

Accordingly, the present invention may be embodied in hardware and/or insoftware (including firmware, resident software, micro-code, etc.).Furthermore, embodiments of the present invention may take the form of acomputer program product on a computer-usable or computer-readablestorage medium having computer-usable or computer-readable program codeembodied in the medium for use by or in connection with an instructionexecution system. In the context of this document, a computer-usable orcomputer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: an electricalconnection having one or more wires, a portable computer diskette, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,and a portable compact disc read-only memory (CD-ROM). Note that thecomputer-usable or computer-readable medium could even be paper oranother suitable medium upon which the program is printed, as theprogram can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory.

“Imaging data” is a spatial distribution of signals, e.g., recorded byan imaging detector. Accordingly, optical images are spatialdistributions of electromagnetic fields comprising the optical waves oflight and can be generally divided into two categories: 1) a“diffraction” image that is based on the spatially coherent distributionof light signals, and 2) a “non-diffraction” image that is based on thenon-coherent distribution of light signals. The feature that separatethese two types of imaging data lie in the existence of the coherenceamong the electromagnetic fields at different spatial locations.Diffraction images are acquired from light signals that are dominated byhighly coherent fields at different locations while non-diffractionimages are acquired from light signals that are dominated by fields oflittle coherence at different locations. Conventional opticalmicroscopy, for example, is a widely used tool to acquirenon-diffraction (or diffraction limited) images of either bright-fieldof elastically scattered light or of fluorescence light signals fromparticles excited with a non-coherent light source; however, the imagesare non-diffractional and inherently two-dimensional replica of thethree-dimensional cell structures in real space with the 3^(rd)dimension compressed into a “focal depth.” In comparison, diffractionimages can be acquired from a particle illuminated or excited with acoherent light source such as a laser and used to extractthree-dimensional morphology parameters about the particle. One exampleof a diffraction image is a hologram which is made by the recording ofthe interference between the elastically scattered light from an objectilluminated with a laser beam and a reference beam split off the laserbeam. The hologram can be illuminated by the same laser beam for viewingits three-dimensional structures. However, advances in image processingtechnology allow diffraction imaging and three-dimensional featureextraction without necessarily requiring use of the reference beam.

Embodiments according to the present invention will now be discussedwith reference to FIGS. 1-21C.

As illustrated in FIG. 1, a microfluidic flow cytometer assembly 10includes a fluid control unit 100, a coherence distribution detector 130and a coherence distribution imaging analyzer 140. The fluid controlunit 100 includes a core fluid inlet 102, a sheath fluid inlet 104 thatprovides a core fluid and a sheath fluid, respectively, to a flow path106 in a microfluidic flow cell 108. The flow cell 108 outputs the corefluid and sheath fluid via a disposal outlet 110. The fluid control unit100 is configured to form a hydro-dynamically focused flow stream havinga core fluid and sheath fluid through the flow path 106 of the flow cell108. A controller controls the flow of the core fluid and sheath fluidinto the inlets 102, 104, respectively. Particles in the core fluid ofthe hydrodynamically focused flow path 106 pass through the flow cell108 substantially one at a time. The core fluid includes particles ofinterest, such as biological cells (including human cells andphytoplankton cells) and other microscopic particles.

The output of a coherent light source, such as a laser beam 120, isconfigured to illuminate a particle in the inner core fluid of the flowcell 108. An imaging detector or coherence distribution detector 130 isconfigured to detect a spatially coherent distribution of elasticityscattered light from the particle. An analyzer 140 is configured toextract three-dimensional morphology parameters of the particle from thespatially coherent distribution of the elastically scattered light.

In this configuration, when a particle in the inner core fluid of theflow cell 108 is illuminated, it scatters light in various directions. Acomplex spatial pattern of the elastically scattered light can be formedthat is dependent on the particle's size, shape, intra-particledistribution of refraction index, and/or morphology. According toembodiments of the present invention, the spatial distribution of theelastically scattered light or the diffraction image data can beacquired, for example, by the detector 130, at an appropriate angularrange to determine various three dimensional morphological parameters ofthe particle. For example, the analyzer 140 can use the diffractionimage data to extract the volume(s) and refractive index (indices)associated with the structure of the particle, such as a volume andrefractive index of the cytoplasm and/or nucleus and/or mitochondrion ina biological cell.

In particular embodiments, the fluid control unit 100 can be a laminarflow controller configured to provide laminar flow at relatively lowvelocities of 10 mm/s or less, which can allow image acquisition with anexposure time of up to 50 μs with relatively small amounts ofdisplacement (such as less than 0.5 μm) of the particle in the innercore fluid of the flow cell 108. In this configuration, the fluidcontrol unit 100 can provide increased signal to noise ratios forenhanced imaging capabilities, including, e.g., diffraction imaging.

In some embodiments, the microfluidic flow cell 108 can be formed of thematerial having an index of refraction that is substantially similar toan index of refraction of the sheath fluid in the flow cell 108, such assilicone or other polymer materials. In this configuration, the noisebackground can be reduced by generally matching the index of refractionof a portion of the flow cell 108 through which the laser 120 isimpinged and the fluid flowing inside the flow cell 108. In addition,the flow cell 108 can have at least one planar side, for example, asshown in FIG. 6, such that light from the laser 120 travelssubstantially perpendicular to the planar side of the flow cell 108 tofurther reduce noise background due to scattering at the entrancesurface of the flow cell 108 and increase the signal to noise ratio.

In some embodiments of the current invention as illustrated in FIGS.2-3, a microfluidic flow cytometer assembly 200 is provided. Theassembly 200 includes a non-coherent light source 202, a laser lightsource 204, a flow cell or microfluidic device 206 (having a cell orother particle flowing therethrough) and a beam splitter 208. Light froma non-coherent light source 202 and the laser light source 204 isimpinged on a particle in a microfluidic device 206. Scattered light andfluorescence light are separated when they pass through the beamsplitter 208. Elastically scattered light from a particle due toexcitation by a laser light beam from the laser light source 204 passesthrough a filter F1 and is detected by an image detector 220 (e.g., acharge coupled device (CCD1) camera) to acquire diffraction image data,and elastically scattered and/or fluorescent light from the sameparticle due to the non-coherent light source 202 is acquired by anotherimage detector 210 (e.g., a charge coupled device (CCD2) camera) asbright-field or fluorescent image data depending on the selection of thewavelength filter F2 (shown in FIG. 2). As illustrated in FIG. 2, lightfrom the non-coherent light source 202 can pass through two lenses L1,L2, a filter wheel FW and a condenser lens CL to focus a light beam fromthe non-coherent light source 202 onto a particle in the microfluidicdevice 206. Elastically scattered and/or fluorescent light from theparticle due to excitation by the non-coherent light source 202 thenpasses through an objective lens 260 (e.g., an infinity-correctedobjective lens) and the beam splitter 208 and through a tube lens TL andfilter F2 before being detected by the non-diffraction image detector210. Elastically scattered light from the laser light source 204 passesthrough the objective lens 260 and the beam splitter 208 through anothertube lens TL and filter F1 before being detected by the diffractionimage detector 220.

In this configuration, the detector 220 acquires diffraction images oflight scatters near a scattering angle, e.g., of about 90° from thedirection of the laser light source 204. The detector 210 can alsoacquire non-diffraction image data due to excitation by the non-coherentlight source 202, such as the non-coherent distribution of elasticallyscattered light signals for bright-field or dark-field and/orfluorescent signals (for example, using fluorescence staining cells withan appropriate filter F2) for fluorescence imaging data. In someembodiments, the diffraction images and the non-diffraction images canbe acquired at substantially the same time and/or combined to provideadditional information about particles in the microfluidic device 206.As would be understood by one of skill in the art, the non-diffractionimaging data can provide a replica image of the particle but withdetails that are limited by the diffraction effect of the residuecoherence in the non-coherent light signals of diffraction limit. Insome embodiments, the non-diffraction imaging data and the diffractionimaging data can be combined, for example, in an image overlay.

For example, as illustrated in FIG. 3, the non-diffraction imaging datafrom the image detector 210 and the diffraction imaging data from thedetector 220 can be used by a feature extraction module 230. Particlesin the microfluidic device 206 can be classified by imaging dataanalysis using a classification module 240. Various classificationtechniques can be used, for example, to estimate the volume or othercharacteristics of features of the particles, such as biological cells.In particular embodiments, calculated diffraction imaging data 250 fromparticles with a known morphology can be used to “train” variousclassification techniques, which can be used to extract threedimensional morphological features for particle classification by theclassification module 240.

In some embodiments shown in FIGS. 4A-4B, another laser light source 270can be used to provide an excitation beam for the detector 220 toacquire diffraction images of elastic light scatters in thebackscattering directions of near 180°. As illustrated in FIGS. 4A-4B,the laser light source 270 produces a laser beam through a beam expander272, a wave plate 274, and an attenuator 276 before passing through thebeam splitter 208 (which is rotated 90° in comparison to the orientationshown in FIG. 2) prior to scattering light to the cell in themicrofluidic device 206 in the backscattering direction.

It should be understood that any suitable light source can be used forthe coherent light sources 204, 270 and the non-coherent light source202. In particular embodiments, the non-coherent light source 202 can bea Kohler illumination system with a 175 W xenon lamp, and the coherentlight sources 204, 270 can be provided by the same or different laserswith one or multiple output wavelengths, e.g., between 180 and 3000 nm.Particular wavelength examples that may be suitable include 444 or 532or 633 nm. The objective lens 260 can be an infinity-corrected objectivelens (M Plan Apo HR 50× or 100×, Mitutoyo) with a relatively largenumerical aperture and long working distance. By using differentdistances between the tube lenses TL behind the objective lens 260 andthe detectors 210, 220, the diffraction and non-diffraction images canbe acquired using the same objective lens 260 as illustrated in FIGS. 2and 4A-4B. For example, a 50× objective lens has a 5.2 mm workingdistance and a 0.75 numerical aperture. This allows the collection oflight scatters within a half angle or width of 48.6° in the air fordiffraction image. If the microfluidic device 206 includes a siliconeside wall interface through which light from the sources 202, 204passes, the half angle width may reduce to about 32° due to the lightrefraction at the air-silicone interface. The detectors 210, 220 can berelatively sensitive EMCCD cameras (DU 885K, Andor technology) of a1004×1002 pixels, which may provide a reduced exposure time. Althoughstandard cooled CCD cameras (e.g., Alta U2000) can be used, EMCCDcameras typically have an electron-multiplying mechanism to amplifysignals before analog to digital conversion. This feature can allow athree to 10 fold increase in the signal-to-noise ratio for weak lightsignals on the order of 10 photons per pixel, thus potentially reducingthe exposure time from values of about 50 μs to about 10 μs or less toallow higher cell speed in the flow and processing throughput. The EMCCDcamera used also has a relatively high pixel readout rate at 35 MHz toachieve a frame transfer rate of 112 frames per second with a 4×4 pixelbinning. The dark-current and readout noises of the EMCCD camera can bea relatively low, which can lead to a large dynamic range of the pixelcount (e.g., 70 dB) and subsequent 14-bit digitization, which may beuseful for more accurate acquisition of diffraction image data. Althoughspecific exemplary angle, pixel numbers, frame speeds, etc. are providedabove, it should be understood that any suitable values can be used.

Accordingly, any suitable cytometer configuration can be used, e.g., toacquire diffraction image data according to embodiments of the presentinvention. For example, cytometer assemblies according to embodiments ofthe present invention can be configured to facilitate the acquisition ofdiffraction images in different angular regions. In addition, a reliableelectronic triggering and delay unit can be used for accurately gatingthe image data acquisition. For example, as shown in FIG. 5, a“shouldered” microfluidic device can be used for acquiringangle-resolved diffraction imaging data centered at 45°.

In some embodiments, the particles are classified by the classificationmodule 204 in FIG. 3 based on the diffraction image data and/or thenon-diffraction image data. For example, the particles can be classifiedby training classification algorithms with a database of diffractionimage data and/or the non-diffraction image data of particles of knownthree-dimensional morphological parameters for various features, whichfor the case of biological cells can include nuclear volume, nuclearshape, nuclear refractive index heterogeneity, nucleus-to-cytoplasmvolume ratio, cell shape, cytoplasm-nucleus refractive index contrast,mitochondria density, mitochondrion-to-cytoplasm index contrast, andmitochondrion-to-nucleus refractive index contrast. Other cell featuresthat can be determined include cell death, binding events, and the like.The training database can be constructed from non-flow-cytometry data bycombing the three dimensional morphology features extracted fromconfocal microscopy imaging data and numerical modeling of elastic lightscattering using the rigorous solution of the Maxwell equations.(references: (1) J. Q. Lu, P. Yang, X. H. Hu, “Simulations of LightScattering from a Biconcave Red Blood Cell Using the FDTD method”,Journal of Biomedical Optics, 10(2), 024022 (2005); (2) R. S. Brock, X.H. Hu, D. A. Weidner, J. R. Mourant, J. Q. Lu, “Effect of Detailed CellStructure on Light Scattering Distribution: FDTD study of a B-cell with3D Structure Reconstructed from Confocal Images”, Journal ofQuantitative Spectroscopy & Radiative Transfer, 102, 25-36 (2006)).

In some embodiments, the morphology parameters of a particle can bedetermined using images acquired at a defocused position with respect tothe particle, as described, for example, in Example 4.

Although embodiments according to the present invention are describedherein with respect to the microfluidic flow cytometer assembly 10 andthe fluid control unit 100 as shown in FIG. 1, it should be understoodthat other fluid control techniques can be used. For example, fluidcontrol units can be provided in which the fluid is controlled withoutbeing fully constrained by a microfluidic device. As shown in FIG. 12, a“jet-in-fluid” based fluid control unit 300 is shown. The fluid controlunit 300 includes a core fluid reservoir 302 for containing the corefluid 302A, a sheath fluid inlet 304, and flow path unit 305 havingthree fluid passages 306A, 306B and 306C. The flow path unit 305 can bea glass cuvette that is filled with a fluid, such as water.

The core fluid reservoir 302 includes a piston 302B for moving the corefluid 302A into the flow path unit 305 such that the core fluid 302A iscombined with the sheath fluid from the sheath fluid inlet 304. The corefluid 302A enters a passage 306A, and sheath fluid enters anotherpassage 306B via the inlet 304 such that the core and sheath fluids arecombined in the passage 306B. The core fluid reservoir 302 also includesa stirring unit 302C for stirring the core fluid 302A. The flow pathunit 305 includes the flow passages 306B and 306C, which have a gap 308therebetween. The flow rate of the fluid and the passages 306A and 306Bcan be configured such that the core fluid flows from the passage 306Ainto the passage 306B to form a laminar flow and then flows through thegap 308 before enters passage 306C followed by an outlet 310. Theobjective 260 of a camera (e.g., a CCD camera) can be positioned so thatthe objective 260 can be used to capture an image of a particle P in theflow path unit 305 when the particle P is in the gap 308, and particlesP in the core fluid 302A of the hydrodynamically focused flow path passthrough the gap 308 substantially one at a time. The core fluid includesparticles of interest, such as biological cells (including human cellsand phytoplankton cells) and other microscopic particles as describedherein.

In this configuration, particle P can be imaged in the gap 308 such thatthe particle P is substantially free from surrounding materials havingmismatched indexes of refraction, such as the solid materials that formthe passages 306A, 306B and 306C.

In some embodiments, the passage 306A can be formed of a stainless steelneedle, for example, having an inner diameter of about 200 μm and anoutside diameter of about 300 μm to guide the core fluid 302A from thereservoir 302 into the passage 306B. The passage 306B can be formed of asquare glass tube T1 having a length of about 8 mm on the interior sidethat connects to a tapered square tube T2 having an interior side lengthof about 80 μm and an exterior side length of about 230 μm. Inside thetube T2 of the passage 306B, the sheath and core fluids form a laminarflow under appropriate pressure differences from syringe pumps thatsupply the fluids. The fluids are then collected by the passage 306Csuch that the particles carried by the laminar flow can remain insidethe core fluid in the gap 308 having a diameter as small as about 100μm. The gap 308 can be about 5 mm. If the fluids in the flow path unit305 and the passages 306A, 306B and 306C have similar indexes ofrefraction (such as when the core fluid, the sheath fluid and the fluidin the unit 305 are all water or water-based), index-mismatchedinterfaces are reduced or eliminated within the field of view (FOV) ofthe objective 260. For example, the objective 260 can be positioned atleast about 13 mm away from the particle P to reduce or eliminate anyindex-mismatches due to the glass of the unit 305.

It should be understood that any suitable orientation of the flow pathunit 305 and objective 260 can be used. For example, the laminar flowingsheath/core fluid in the passage 306B can enter the flow path unit 305from the top or from a side of the unit 305 rather than from the bottomof the unit 305 as shown in FIG. 12.

Therefore, an image captured through the objective 260 (as describedwith respect to FIGS. 2, 3, 4A-4B and 5) may have improved image qualitydue to the reduction in index-mismatched materials in the regionadjacent the particle P. When compared to a microfluidic flow cell suchas the flow cell 108, the particle P in the gap 308 containssubstantially no optical heterogeneity near the particle P. One or morelaser beams can be introduced from the plane side surfaces of the flowpath unit 305 to excite the particle P, and the microscope objective 260of a camera (e.g., a CCD camera) can be used to collect and recordscattered light distribution from another side of the flow path unit305. In some embodiments the objective 260 can be positioned with afield-of-view that is far from the cuvette side surfaces (e.g., about10-15 mm from the particle P). The jet-in-fluid design of the passages306B and 306C and the gap 308 can reduce or eliminate theindex-mismatched interfaces close to the flowing particle P whileprovide fluid flow control similar to that of a flow cyometer, e.g., toallow high throughput assay and/or multiple excitation beams.

Additional embodiments according to the present invention will now bediscussed with respect to the following non-limiting examples.

EXAMPLE 1

To increase the morphology information that can be retrieved fromelastic light scatter due to excitation by a coherent light source, aprototype microfluidic flow cytometer was constructed to test theconcept of diffraction image acquisition with a standard cooled CCDimager (Alta U2000). The prototype is shown in FIGS. 6 a-6 d. Apreliminary study of diffraction images with polystyrene microsphereshas been performed. The prototype can have the specifications as setforth in Table 1.

TABLE 1 The expected specifications of the proposed dual-imagemicrofluidic flow cytometer Adjustable flow speed 0.01~0.50 m/sAdjustable core fluid diameter 20~150 μm Half-width of angular regionfor diffraction image* 32° Center of angular region for diffractionimage 5°, 45°-135°, 180° Lateral resolution of the non-diffractionimage* 0.4 μm Field of view of non-diffraction image* 320 μm × 240 μmCCD Camera exposure time 10-10,000 μs Image frame transfer rate (251 ×250 pixels 112 frames/s per image frame) SNR of CCD image{circumflexover ( )} ~3000 *Assuming and infinity-corrected objectives lens of 50×,working distance = 5.2 mm, NA = 0.75 at λ = 633 nm. {circumflex over( )}SNR = signal-to-noise ratio of EMCCD camera: signal = well depth,noise = dark current noise + readout noise.

As illustrated in FIG. 6 a, two glass syringes with precisely andindependently controlled moving pistons form a flow control unit usinggears and stepping motors. The syringes function as the reservoirs ofsheath and core fluids to generate a laminar flow in a siliconemicrofluidic device by applying an appropriate pressure by the movingpistons. Silicone polymer of a refractive index 1.41 at 633 nm ishardened in a mold with long glass fibers to make microfluidic deviceswith a smooth-walled microchannel with diameter variable between 20 and200 μm. The microchannel is connected to the flow control unit with astandard flow cytometer nozzle to allow a stable laminar flow with alength of about 80 mm. Several microfluidic device designs have beentested to establish laminar flows with different core fluid diameter andflow speed. Compared to the conventional flow cytometer, the siliconepolymer based microfluidic device has several features: very longdistance of laminar flow (80 mm vs 10 mm), low core flow speed (0.01 m/sto 1 m/s vs 10 m/s) and nearly matched refractive indices between thesheath fluid and surrounding medium (0.02 or less between glycerol/watermixer of sheath fluid and silicone vs 0.2 between water and glass). Thelast two features may result in the achievement of relatively slowexposure times of up to 50 μs with a standard cooled CCD camera on theslow-moving particle (which moves 0.2 μm at a speed of 0.01 m/s) and/orreducing the noise background due to the scattered light at theflow-silicone interface.

To evaluate the microfluidic flow cytometer, an imaging system wasdesigned to acquire diffraction images of microspheres of 25 μm diameterinterrogated by a 633 nm laser beam. An infinity-corrected objectivelens (Plan Apo 50×, Mitutoyo) was used to acquire diffraction images bya cooled CCD camera. A Kohler illumination with a xenon arc lampprovides a non-coherent light source (NCLS) for acquisition ofbright-field images for system alignment. The diffraction images oflight scatters were acquired with the NCLS blocked. With the objectiveof a 13 mm working distance and 0.55 numerical aperture (NA), theacquired scatter image corresponds to the angular ranges of scatter ofθ_(s) and φ_(s) (see FIG. 7 for definitions) between 70° and 110° fromthe incident direction of laser beam. The Mie theory of light scatteringby spheres was used to obtain the calculated diffraction image in FIG. 8b with the refractive indices of polystyrene and water (as the hostmedium) at 633 nm for comparison to the measured one shown in FIG. 8 a.The field-of-view of the calculated image was determined from theangular range defined by the objectives lens's numerical aperture (NA)and working distance. It can be noted that the measured and calculatedimages agree well on the characteristic oscillatory pattern of lightintensity. Improvement of the imaging optics may further reduce thebackground noise.

EXAMPLE 2

Coherent scattering of a monochromatic light beam occurs as a dominantform of interaction when the refractive index n becomes heterogeneous,n(r,λ), where r is the position vector inside a particle and λ is thewavelength for both incident and scattered light, shown schematically inFIG. 9. The scattered fields lead to the spatially coherent andcharacteristic scatter distributions which can be acquired asdiffraction (spatial) or speckle (spatial and temporal) images. Withaccurate wave models based on the solution of the Maxwell equations orwave model trained pattern recognition software, it is possible toextract intra-particle refractive index distribution and thusthree-dimensional morphology information of the scatterer or cells fromthe elastic light scatter distribution or diffraction image data. Theintra-particle index distribution of n(r,λ) correlates with cell'smorphology. (Ref: R. Baer, “Phase contrast and interference microscopyin cytology,” in Physical Techniques in Biological Research, edited byA. W. Pollister (Academic Press, New York, 1966), Vol. 3, pp. Ch. 1.)Most of biological cells have size parameters α (=2πa/λ) ranging between1 and 100 for UV/visible and near-infrared light with 2a as the meandiameter (see FIG. 9). Accurate modeling of light scattering requiresnumerical calculation of the scattered electromagnetic fields usingrealistic three-dimensional cell structures that can be acquired with,for example, the confocal imaging technique. Recent progress innumerical modeling has led to new tools to study the correlation betweencell morphology and angle-resolved light scatter distribution ordiffraction image. These include the development of parallel codes basedon the finite-difference-time-domain (FDTD) and discrete dipoleapproximation (DDA) algorithms on low-cost parallel computing clusters.(Ref: J. Q. Lu, P. Yang, X. H Hu, “Simulations of Light Scattering froma Biconcave Red Blood Cell Using the FDTD method,” J. Biomed. Opt., 10,024022 (2005); R. S. Brock, X. H. Hu, P. Yang, J. Q. Lu, “Evaluation ofa parallel FDTD code and application to modeling of light scattering bydeformed red blood cells,” Opt. Express, 13, 5279-5292 (2005); M. A.Yurkin, K. A. Semyanov, P. A. Tarasov, A. V. Chernyshev, A. G. Hoekstra,V. P. Maltsev, “Experimental and theoretical study of light scatteringby individual mature red blood cells by use of scanning flow cytometryand a discrete dipole approximation,” Appl Opt, 44, 5249-56 (2005); M.A. Yurkin, A. G. Hoekstra, R. S. Brock, J. Q. Lu, “Systematic comparisonof the discrete dipole approximation and the finite difference timedomain method for large dielectric scatterers,” Opt. Express, 15,17902-17911 (2007)). In addition, methods to construct three dimensionalstructure of cells based on their confocal images for FDTD or DDAsimulations have been developed. (Ref: R. S. Brock, X. H Hu, D. A.Weidner, J. R. Mourant, J. Q. Lu, “Effect of Detailed Cell Structure onLight Scattering Distribution: FDTD study of a B-cell with 3D StructureConstructed from Confocal Images,” J. Quant. Spectrosc. Radiat.Transfer, 102, 25-36 (2006); H. R. Hurwitz, J. Hozier, T. LeBien, J.Minowada, K. Gajl-Peczalska, I. Kubonishi, I. Kersey, “Characterizationof a leukemic cell line of the pre-B phenotype,” Int. J. Cancer, 23,174-180 (1979).)

A parallel FDTD code to simulate the spatially coherent distribution ofelastic light scattering by B-lymphocyte cell line NALM-6 cells has beendeveloped that can used, combined with the three dimensional morphologyof these cells, to construct a database to train the softwareclassification module. The three dimensional structures of stainedNALM-6 cells were constructed from their confocal images with a nucleardye (Syto 61, Invitrogen). FIG. 10 is a table including the threedimensional structural parameters of seven NALM-6 cells. By importingthe three dimensional structures into the FDTD code, various Muellermatrix elements were obtained, of which the element S₁₁ represents thescattered light intensity normalized by the unpolarized incident beamintensity. The following were assumed in the simulations: (1) both thenucleus and the cytoplasm regions are homogeneous; (2) the incidentwavelength λ₀=1 μm in vacuum; (3) the cytoplasm has a complex refractiveindex of n_(c)=1.3675+i1.0×10⁻⁵ while nuclear index is either n_(n)=1.42or 1.46 (Ref: J. R. Mourant, M. Canpolat, C. Brocker, O. Esponda-Ramos,T. M. Johnson, A. Matanock, K. Stetter, J. P. Freyer, “Light scatteringfrom cells: the contribution of the nucleus and the effects ofproliferative status,” J Biomed Opt, 5, 131-7 (2000)); (4) each cell issuspended in a host medium with a refractive index of n_(h)=1.35. TheFDTD grid cell size was set at Δx=λ/20 with λ=λ0/n_(h). To study thedependence of light scatter on cell orientation, 12 sets of orientationangles of the cell (θ₀, ω₀) were selected, covering the 4π solid angleuniformly.

FIG. 11 presents three plots of 9 NALM-6 cells distributed in the twodimensional feature subspaces defined by the light scatter of elementS11 at the scattering polar angle θ_(s)=0° versus its integrated valuein different angular range of θ_(s) as side scatters. These are based onthe seven cells shown in FIG. 10 with 2 additional cells obtained byassigning high nuclear index for the #9 and #10 cell. The dotsrepresenting the same cell are for different orientations. It can befirst observed that the dots of the same cell with differentorientations tend to cluster together. This demonstrates that the lightscatter distribution is insensitive to the cell orientation. The forwardscatter intensity does not exhibit a simple linear relation as oftenexpected by the conventional view. Furthermore, as shown in the firsttwo plots of FIG. 10, the cluster of cells in their dividing stages withsplitting nuclei (cells #10 and #9) are separated from the cluster ofthe cells with regular shaped nucleus (cells #1, 3, 7, 2, and 8) and thecluster with higher nuclear index of refraction. These results show thatdetailed analysis of angle-resolved scatter distribution or diffractionimage data can provide rich information on cell morphology and indexdistribution, and an accurate modeling tool can produce a trainingdatabase for development of a robust pattern recognition software forextracting 3D morphology features rapidly.

EXAMPLE 3

Forty cultured NALM-6 cells in different cell cycles are selected as acell model to establish a training database for cell classification. Thecells are stained and imaged with a confocal microscope (LSM 510,Zeiss). The three dimensional structures will be constructed for twopurposes. First, these structures will be imported into our FDTD codefor simulations of angle-resolved elastic light scatter from the cellexcited by a coherent light beam. Second, these structures will beanalyzed to define multiple classes of morphology features as the basisfor development of the pattern recognition software for cellclassification. Light scattering by a single biological cell can bemodeled as a plane wave incident on a dielectric scatterer in a hostmedium. To account for the polarization change associated with scatteredlight, a framework of Stokes vectors is adopted for the incident andscattered light fields and a Mueller matrix for the cell.

The incident light is represented by its electromagnetic fields of (E₀,H₀) with E representing the electric field and H the magnetic field witha wave vector k₀ while the scattered light by (E_(s), H_(s)) with k(|k₀|=|k|=2π/λ), as shown in FIG. 7. The host medium, such as thelaminar fluids, is characterized by a refractive index n_(h) and thecell by a spatially variant refractive index, n(r, λ), corresponding toits three dimensional structure. Because of the linear relation betweenthe incident and scattering fields, a complex 2×2 amplitude matrixdescribes the effect of cell with the fields separated into componentsparallel and perpendicular to the scattering plane defined by k andk_(o)

$\begin{matrix}{\begin{pmatrix}E_{\parallel S} \\E_{\bot S}\end{pmatrix} = {\frac{^{{\; {kr}} + {\; {kz}}}}{{- }\; {kr}}\begin{pmatrix}S_{2} & S_{3} \\S_{4} & S_{1}\end{pmatrix}\begin{pmatrix}E_{\parallel O} \\E_{\bot 0}\end{pmatrix}}} & (1)\end{matrix}$

Since the 2×2 matrix elements are complex and related to fieldamplitudes, a real 4×4 Mueller matrix S that relates directly to themeasurable intensity signals of light can be defined. The Mueller matrixis defined as

$\begin{matrix}{{\begin{pmatrix}I_{S} \\Q_{S} \\U_{S} \\V_{S}\end{pmatrix} = {\frac{1}{k^{2}r^{2}}\begin{pmatrix}S_{11} & S_{12} & S_{13} & S_{14} \\S_{21} & S_{22} & S_{23} & S_{24} \\S_{31} & S_{32} & S_{33} & S_{34} \\S_{41} & S_{42} & S_{43} & S_{44}\end{pmatrix}\begin{pmatrix}I_{O} \\Q_{O} \\U_{O} \\V_{O}\end{pmatrix}}},} & (2)\end{matrix}$

where I, Q, U, V are the Stokes parameters of the incident light (withsubscript 0) and the scattered light (with subscript s). All matrixelements are function of scatter angles of (θ_(s), φ_(s)) and cellorientation angles of (θ₀, φ₀). The element S₁₁ yields the probabilityof an unpolarized incident light being scattered into the direction ofθ_(s) and φ_(s) while other elements provide information on scatteredlight with different polarization states.

Given the incident fields (E, H) at λ and n(r, λ), based on the threedimensional structure of the cell with assigned indices for differentintracellular components, the Mueller matrix elements S_(ij) can becalculated using the numerical method of FDTD or DDA. (Ref: J. Q. Lu, P.Yang, X. H Hu, “Simulations of Light Scattering from a Biconcave RedBlood Cell Using the FDTD method,” J. Biomed. Opt., 10, 024022 (2005);R. S. Brock, X. H. Hu, P. Yang, J. Q. Lu, “Evaluation of a parallel FDTDcode and application to modeling of light scattering by deformed redblood cells,” Opt. Express, 13, 5279-5292 (2005); R. S. Brock, X. H Hu,D. A. Weidner, J. R. Mourant, J. Q. Lu, “Effect of Detailed CellStructure on Light Scattering Distribution: FDTD study of a B-cell with3D Structure Constructed from Confocal Images,” J. Quant. Spectrosc.Radiat. Transfer, 102, 25-36 (2006); H. Ding, J. Q. Lu, R. S. Brock, T.J. McConnell, J. F. Ojeda, K. M. Jacobs, X. H. Hu, “Angle-resolvedMueller Matrix Study of Light Scattering by B-Cells at Three Wavelengthsof 442, 633 and 850 nm,” J. Biomed. Opt., 12, 034032 (2007)). It hasrecently been shown that the DDA algorithm can be 10 to 100-fold fasterthan FDTD algorithm for large cells such as the NAM-6. (Ref: M. A.Yurkin, A. G. Hoekstra, R. S. Brock, J. Q. Lu, “Systematic comparison ofthe discrete dipole approximation and the finite difference time domainmethod for large dielectric scatterers,” Opt. Express, 15, 17902-17911(2007)). The database for training software classification moduleincludes, but not limited to, the diffraction images of S₁₁, S₁₂, S₂₂and S₃₄ calculated from the NALM-6 cells excited by coherent laser beamsat, e.g., three wavelengths of 444, 532 and 633 nm and the threedimensional structure of these cells constructed from their confocalimages.

Accurate calculation of diffraction images, which is S_(ij)(θ, φ)projected to the CCD sensor plane of (x, y), may be acquired using arealistic three dimensional structures of the cell as a scatterer. Thethree dimensional structures of cells can be constructed from theirconfocal image stacks with index values assigned, and the data can beimported into a simulation such as the FDTD or DDA software code. Forexample, a red nucleic acid dye (Syto 61, Invitrogen) is used to stainthe cells before confocal imaging. Histogram analysis of the confocalimage pixels separates them into three groups according to their redfluorescence emission intensities: those pixels in the host medium ofPBS (after washing), pixels in cytoplasm and those inside the nuclearmembrane. By tracing the boundaries between pixel groups in eachconfocal image, contours are obtained for the nuclear and cytoplasmicmembranes. These contours are connected through different image planesalong the z-axis of the confocal microscope, the three dimensionalstructures of cytoplasm and nucleus are constructed and different valuesof refractive index are assigned for n(r, λ). The values of n(r, λ) arebased on fitting the Mueller matrix elements S_(ij) to experimentaldata. (Ref: A. Zharinov, P. Tarasov, A. Shvalov, K. Semyanov, D. R. vanBockstaele, V. P. Maltsev, “A study of light scattering of mononuclearblood cells with scanning flow cytometry,” JQSRT, 102, 121-128 (2006);H. Ding, J. Q. Lu, R. S. Brock, T. J. McConnell, J. F. Ojeda, K. M.Jacobs, X. H. Hu, “Angle-resolved Mueller Matrix Study of LightScattering by B-Cells at Three Wavelengths of 442, 633 and 850 nm,” J.Biomed. Opt., 12, 034032 (2007)).

Recently, the Mueller matrix elements S_(ij) have been measured for bothhuman promyelocytic leukemia NALM-6 and HL-60 cell suspensions using agoniometer configuration. For some elements, such as S₁₂ and S₃₄,experimental data indicated large differences between these two celltypes even though their 3D structures exhibit high similarity. Testresults demonstrate that the introduction of mitochondria may accountfor the observed difference in S_(ij). To develop a robust patternrecognition software, the three dimensional structure construction ofthe NAM-6 cells can be enhanced by using two different dyes (Syto 61 andMitoTracker Orage, Invitrogen or other combination) to co-stain thenucleus and mitochondria with different emission wavelength bands beforeimaging. This allows inclusion of both components in the threedimensional structures for accurate simulation of diffraction images.

While the numerical modeling tools discussed above may be highlyaccurate, the intensive computing can take 1 or 2 hours to calculate onediffraction image from an imported cell structure on a 16-node computingclusters. Inverse determination of morphology structures and refractiveindices from measured diffraction image may be a very slow process andnot practical for analyzing large cell populations. Accordingly, apattern recognition or particle classification software module trainedby a database established using the techniques discussed above can beused to rapidly extract multiple three dimensional morphology featuresand refractive indices from the dual image data (i.e., non-diffractionimage data and diffraction image data) for particle classification. Thepattern recognition or particle classification software can include twoparts. The first part extracts the orientation and two dimensionalmorphology parameters as the fiduciary marks from the non-diffractionimage of bright-field or fluorescence and molecular features for stainedcells excited by the non-coherent light source, and the second partextracts three dimensional morphology features from the particle excitedby the coherent light source to build a classification vector with up to20 components for cell classification. Each morphology component of theclassification vector is related to an aspect of the three dimensionalmorphology of the flown particle, which in the cases of biological cellsincludes nuclear volume, nuclear shape, nuclear index heterogeneity,nucleus-to-cytoplasm volume ratio, cell shape, cytoplasm-nucleus indexcontrast, mitochondria density, mitochondrion-to-cytoplasm indexcontrast, and mitochondrion-to-nucleus index contrast. The fiduciarymarks from the first part of the classification software module will beused in the second part as the constraints to reduce the probability ofmisclassification. The second part of the classification softwareincludes two components: (1) a feature extractor to extract threedimensional morphology feature from a diffraction image; and (2) aclassifier to generate a classification vector. A feature extractorselects multiple three dimensional morphology features from diffractionimage data for classification of a large cell population. One exemplarydesign of a feature extractor for the classification software discussedbelow is based on the Support Vector Machines (SVM) algorithm (Ref: I.Guyon, J. Weston, S. Barnhill, V. Vapnik, “Gene selection for cancerclassification using support vector machines,” Mach. Learn., 46, 389-422(2002)); however, other algorithms such as neural networks andstatistical pattern recognition can be used. SVM is a supervised leaningtool that allows classification of image data in an image space definedby a kernel function. It can solve a classification problem with anoptimization process to identify a maximum margin hyperplane thatseparates the image data from a training database into multipleinstances or classes. The hyperplane is based on a set of boundarytraining instances or support vectors. The optimization problem can beformulated by an objective function as the metric to measure theprogress, which also allows treatment of non-separable data bypenalizing misclassifications. Once the hyperplane is established withthe training data, the NAM-6 cell image data acquired will be classifiedaccording to locations in the high-dimension space in relation to thehyperplane. SVM has been successfully used for multi-classclassification of complex biological systems such as the classificationof multiple tumor types based on genes. SVM often performs better thanother methods for classification problems in an image space of highdimension with very few samples per class.

The classification/pattern recognition software will generate aclassification vector with multiple components based on the threedimensional morphology and molecular features as discussed above. Foreach component, an SVM will be built to compute its morphology-relatedvalue. In the following, the procedures related to extraction of aclassification vector component associated with the nuclear volume aredescribed, which serve as an example to illustrate the proposed SVMbased methodology. Other components associated with three dimensionalmorphology features may be treated similarly, including additionalmorphology features not mentioned below. The procedures described beloware designed for generating a component associated with the nuclearvolume from a diffraction image data of NALM-6 cells. Other cell typescan be treated by the same approach using corresponding trainingdatabase.

1. Nuclear volume scaling. The output of an SVM for the componentdetermined from a diffraction image is not nuclear volume Vn: rather, itis a classification value (CR). For a CR scale of 10, CR=0.1, 0.2, 0.3 .. . , 1.0, the range of Vn is divided into 10 value sections. If theminimal and maximal values of Vn for the cells in the training databaseare 200 μm³ and 2000 μm³, respectively, then cells having Vn≦200 μm³ areclassified as the instances of CR=0.1, and those with Vn≧2000 μm³ areclassified as CR=1.0 while rest of the cells are classified as instancesof CR values equally partitioned between 0.1 and 1.0 according to Vn.Development of other classification vector components is similar.

2. Labeling training database. Each diffraction image in the trainingdatabase is labeled with a classification vector based on theirmorphology parameters extracted from the 3D structure constructed fromthe confocal images. A software tool can be developed to automaticallylabel each image. The tool will perform statistical analysis to obtainthe range value of each parameter from the database to determineappropriate instances of the component values and automatically map anabsolute parameter value to a CR value for the component.

3. Image vector generation in an image space. Each diffraction image oflight scatter S_(ij)(θ_(s), φ_(s)) (256×256 pixels) forms an imagevector of 256×256 elements in an image space defined by an appropriatekernel function. In the simplest case, an image space can be formed bythe pixel coordinates, x and y as projected from the scattering anglesθ_(s) and φ_(s) to the CCD sensor plane, with a dimension of 65,536. Inthis image space, the image vector is located by its pixels of (k, z),where k=256x′+y′ with (x′, y′) as the renormalized pixel coordinatesbetween 1 and 256 and 0≦z≦1 as the normalized value of S_(ij).

4. Classification algorithm. The study of SVM based algorithm begins byprojecting all image vectors in the image space with the selected kernelfunction for diffraction images in the training database of NALM-6cells. The SVM algorithm will then proceed to establish a hyperplanethrough a learning process that separates or discriminates image vectorsinto classes with appropriate class labels as defined in Procedure 2above. After completion of the classification for the diffraction imagesfrom the training database, the SVM algorithm will be applied toclassify new image vectors according to its location relative to theestablished hyperplane in the image space.

5. Generation of classification vector. Various tools can be used todevelop an effective and efficient SVM classifier for each componentassociated with a three dimensional morphology feature. The results ofclassification on each of the three dimensional morphology features willbe combined with molecular features, available for stained cells, togenerate a classification vector for each cell in the studiedpopulation. Different kernel functions can be developed to achieveincreased or optimized classification for each of the classificationvector component. A kernel function is used to transform the diffractionimages from the original CCD sensor plane coordinates of (x, y) toanother space as the image space. A good kernel function can improveclassification with an image space in which all image vectors aggregatein separate clusters for different classes. Possible kernel functions,including polynomial or Gaussian function and wavelet transform, will bestudied to evaluate their performance in the proposed research.

The SVM based method described herein employs an one-versus-all (OVA)scheme in combing multiple binary classifiers to make a decision forclassification. In the example of nuclear volume discussed above, 10binary classifiers will be constructed in relation to the supportvectors defined by the 3D structure in the training database. For eachimage vector, its distances to all support vectors in the image spaceare calculated so that a confidence measure (CM) of the binaryclassifier to a particular support vector can be obtained. The CM valueranges between −1.0 and 1.0 which correspond to largest and smallestdistances to the support vector, respectively, and each CM indicates ifthe imaging vector belongs to a class (CM>0) or not (CM<0). The OVAscheme refers to the classification rule of assigning a cell to theclass whose CM value is the largest among all the CM values. Table 2below shows 3 examples of classification based on this scheme.

TABLE 2 The CM value table and OVA classification rule CR scale 0.1 0.20.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 class Cell 0.8 0.5 0.3 0.4 −0.1 −0.2−0.3 0.1 0.2 −0.3 0.1 image 1 Cell 0.1 −0.5 0.2 0.3 −0.1 −0.2 0.3 0.70.2 −0.4 0.8 image 2 Cell −0.7 −0.4 −0.7 −0.4 0.4 −0.9 −0.3 0.1 0.1 −0.30.5 image 3Additionally, acquisition signals and raw results such as images may berepresented following the Flow Cytometry Standard (FCS) data fileformat, which was adopted by the International Society for AnalyticalCytology (ISAC), to promote cytometer data exchange andinteroperability. A software utility for representing and displayingdata based on XML format or other suitable formats can be used.

Embodiments according to the present invention can acquire diffractionand non-diffraction imaging data of particles excited with coherent andnon-coherent light sources by elastic light scatters and/or fluorescencesignals using microfluidic flow cytometry. This can allow the extractionof three dimensional morphological features simultaneously with themolecular features from the fluorescence signals from stained particlesand thus can open the possibility of single-particle analysis of largepopulations in a multi-dimensional feature space previously notavailable. The benefits of three dimensional morphology features can bemanifest through the example of cell death study. Cell death, defined asthe irreversible loss of plasma membrane integrity, can occur innumerous ways according to different morphology and molecularcharacteristics. The three major types of cell death in mammalian cellsare necrosis, apoptosis and autophagy. Apoptosis and autophagy aredifferent forms of programmed death triggered by intrinsic pathways:apoptosis is characterized mainly by changes in nuclear morphologyfollowed by blebbing of the plasma membrane and overall cell shrinkagewhile autophagy is characterized by a massive accumulation oftwo-membrane vacuoles in the cytoplasm followed by fusing withlysosomes. In contrast, necrosis is a response to external andincidental stimuli by cytoplasmic swelling and dilation of cytoplasmicorganelles. As a high throughput instrument, flow cytometry techniquesas described herein may be capable of quantitative assay of death modeand associated time courses in large and heterogeneous cell populationsby extraction of three dimensional morphology and molecular parameters.

EXAMPLE 4

The effect of the objective location on the diffraction imaging has beeninvestigated using the system of FIG. 4B. Once the specimen had beenlocated, the objective 260 was first aligned into the focused positionunder white light illumination. This position was employed for laterimage acquisition as the reference position or x=0. FIGS. 13A-13Cpresents three examples of these non-coherent images acquired under thewhite light illumination at x=0.

After the system alignment, the laser beam was introduced fordiffraction imaging and the imaging system was translated from thereference position of x=0 and diffraction images were taken at each stopwith exposure times ranging from 50 μs to 3 s. The direction of x>0refers to moving the objective towards the specimen. FIG. 14 displays aset of diffraction images acquired at x between −500 μm and +500 μm froma sphere of 25 μm in diameter. These image data exhibit two features.First the images acquired at x>0 present the characteristic verticalfringe pattern one expects from the Mie theory of light scattering bysingle spheres. Further, and counterintuitively, the images acquired atincreasing x values exhibit expanded fringes in the FOV of the camera.Second the images acquired at x<0 contain fringe patterns that arelargely independent of the specimen's structure as the objective ismoved away from the specimen.

In addition to the sphere of 25 μm in diameter, imaging of polystyrenespheres of 9.6 μm in diameter and 4 melanoma cells using the samesequence of translating the objective at different x positions wascarried out. The dependence of the diffraction images exhibit similarfeatures as discussed above on the two sides of x=0. After analysis ofthe sphere image data, it was determined that the diffraction imagesacquired at x=200 μm correlate strongly with the particle morphology andexamples of these images are presented in FIG. 15 together with thoseacquired at x=−200 μm as comparison.

To understand these diffraction image data, the angle-resolved scatteredlight distribution based on the Mie theory (see C. F. Bohren and D. R.Huffman, Absorption and Scattering of Light by Small Particles, p. 447(Wiley, New York 1983)) was calculated and projected the results on ay-z plane as calculated diffraction images without consideration of theobjective and tube lens. The refractive indices of 1.59 and 1.40 wereused for the sphere and host medium of gel (comprising water, glycerol,and hydroxyethyl cellulose), respectively, for the wavelength of λ=532nm. Different values of the angular distance Θ were used in thecalculated diffraction images, which corresponds to the half-widthangular distance of FOV along the horizontal direction or y-axis. Thecalculated and measured diffraction images are shown in FIG. 16 forspheres of the two different diameters.

In obtaining the calculated images, the microscope objective was notconsidered and the only variable was the angular distance Θ, withdecreasing Θ corresponding to expanded fringes in FOV. Comparing of thecalculated images at different Θ with the measured images acquired atx=200 μm, as shown in FIG. 16, shows that a best fit is achieved withΘ=16° if the number of fringes in FOV are used as the criterion. Notethat the half cone angle θ corresponding to the objective placed at thefocused position (x=0) is 23° based on the objective's NA (=0.55) andthe refractive index of the gel (n=1.40). The result of Θ=16° for thecalculated image is comparable to the diffraction image acquired withthe objective moved towards the sphere from x=0 by 200 μm, which isunexpected and should be due to the defocused objective. The fringes inthe calculated images do not curl near the edges of FOV as those in themeasured one which may be attributed to the effect of the objective aswell.

The morphological changes induced in the B16F10 cells were examined todetermine if these cells can be used as a model for study of thecorrelation between diffraction images and 3D morphological features. Itwas observed that the expression of the G protein-coupled receptor,GPR4, in B16F10 cells increased the formation of dendrites and led tochanges in morphology. Furthermore, it has been found that GPR4expression increased melanin content by 4 fold by direct measurement ofmelanin concentration with a spectrophotometer (L. V. Yang et al.unpublished data) due to the increased production of melanin particles,which are markers of melanocyte differentiation. An assay of 10,000cells has been performed with a conventional flow cytometer (FACScan,Becton Dickinson) for each of the two cell types and the plots of lightscatter are presented in FIGS. 17A-17B. From these data, one can observethat the mean value of the foward scatters by the B16/vector cells (meanvalue=391) are slightly larger than that by the B16/GPR4 cells (meanvalue=375) while the relation in side scatters is reversed (228 vs 414).

To quantitatively investigate the morphological changes, 3Dreconstruction of melanoma cells has been performed with developedsoftware through confocal imaging. The pixels in the confocal imagestacks were first sorted into three groups: cytoplasm, nucleus andmitochondria according to the intensity and wavelength of fluorescenceemission. Then the contours of related organelles between neighbouringslices were connected for 3D structure and volume calculation. Oneexample of 3D structure for a B16/GPR4 cell is presented in FIG. 18 intwo sectional views. Five cells from each of the two cell types wererandomly selected to acquire confocal image stacks for 3Dreconstruction. The extracted volume data are listed in Table 3 below.Further study of the molecular mechanisms and functional significance ofGPR4-induced morphological change and melanin production anddifferentiation in B16F10 cells are currently under investigation.

TABLE 3 The volume parameters of B16F10 cells (μm³) B16/GPR4 B16/vectornuclear * 1103 ± 307 1164 ± 533  mitochondria * 206 ± 63 297 ± 107cell * # 3586 ± 892 4910 ± 2170 N/C {circumflex over ( )} 31% 24% Meanradius (μm) 9.50 10.5 * in the form of mean ± std with 5 cells in eachgroup. # equal to the volume sum of cytoplasm, nucleus and mitochondria.{circumflex over ( )} mean volume ratio of nucleus-to-cell.

It is interesting to note that the cytoplasm volumes of the two celltypes appear to be different while the volumes of the nucleus andmitochondria between the two types are close in values. The fact thatB16/vector cells have larger cytoplasm volume may be used to explain thefeature revealed by the plots of light scatters in FIGS. 17A-17B, whichcan be interpreted as the B16/vector cells having larger cell volume andless intracellular heterogeneity. Furthermore, the variation incytoplasm volume, induced by the GPR4 receptor, may suggest asignificant difference between the cytoskeletons of these cells whichcan have important consequences in cells' ability for adhesion andmigration. While a conventional flow cytometer can be used to acquirelight scattering data as shown in FIGS. 17A-17B, one can clearly seethat these data provide very limited quantitative information on 3Dmorphology. In contrast, the diffraction images presented hereindemonstrate large difference between spheres of different sizes andspheres and cells of similar sizes and thus can be used to extract 3Dmorphological features as we discussed in the introduction. A study ofdifferent B16F10 cell populations with a diffraction imaging flowcytometer and comparison with FDTD numerical modelling may be conducted.

It should be understood that the techniques described herein can be usedwith respect to particles in a fluid control unit, such as the fluidcontrol unit of FIG. 12. FIGS. 19, 20 and 21A-21C are digital images ofspheres taken using the fluid control unit of FIG. 12. In particular,FIG. 19 illustrates digital images of 9.6 μm diameter spheres in 532 nmlight with a flow speed of between about 1.6 mm/s and 1.8 mm/s and anexposure rate of 50 μs. FIG. 20 illustrates digital images of 9.6 μmdiameter spheres in 532 nm light with a flow speed of about 12 mm/s andan exposure rate of 50 μs. FIG. 21A is a digital image of a 5.2 μmdiameter spheres with a flow speed of between about 4.7 mm/s. FIG. 21Bis a digital image of a 9.6 μm diameter sphere with a flow speed ofabout 12 mm/s. FIG. 21C is a digital image of a 25 μm diameter spherewith a flow speed of about 7 mm/s.

One potential disadvantage of the current technology based on integratedlight signals lies in its potentially limited capability in extractingmorphological features which carry rich information on cells.Experimental results are presented related to the diffraction imaging ofsingle specimen embedded in gel with a microscope objective. The resultsshow that diffraction images acquired with an objective correlate strongcorrelation with the 3D morphology of the specimen if these images aretaken at a defocused position towards the specimen. The 3D structures oftumorigenically transformed B16F10 melanoma cells have beeninvestigated, and it has been established that these cells can be usedas a model for study of 3D morphology with the diffraction imagingmethod. With the flow-in-fluid techniques described herein, the methodof diffraction imaging with a microscope objective can be implemented ina flow cytometer to acquire rapidly image data for extraction of 3Dmorphological features, for example, at a rate of 10² cells per second.

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. Therefore, it is to be understood that the foregoing isillustrative of the present invention and is not to be construed aslimited to the specific embodiments disclosed, and that modifications tothe disclosed embodiments, as well as other embodiments, are intended tobe included within the scope of the appended claims. The invention isdefined by the following claims, with equivalents of the claims to beincluded therein.

1. A flow cytometer assembly, comprising: a fluid controller configuredto form a hydrodynamically focused flow stream, the stream including anouter sheath fluid and an inner core fluid; a coherent light sourceconfigured to illuminate a particle in the inner core fluid; a detectorconfigured to detect a spatially coherent distribution of elasticallyscattered light from the particle excited by the coherent light source;and an analyzing module configured to extract a three-dimensionalmorphology parameter of the particle from a spatially coherentdistribution of the elastically scattered light.
 2. The cytometerassembly of claim 1, wherein the fluid controller comprises a firstfluid passageway, a second fluid passageway and a fluid-filled gapbetween the first and second fluid passageway, wherein the detector isconfigure to detect the spatially coherent distribution of elasticallyscattered light from the particle excited by the coherent light sourcewhen the particle is in the gap between the first and second fluidpassageways.
 3. The cytometer assembly of claim 1, wherein the detectoris further configured to provide diffraction image data of the particlecomprising the spatially coherent distribution of the elasticallyscattered light.
 4. The cytometer assembly of claim 3, furthercomprising a non-coherent light source configured to illuminate theparticle and a detector configured to detect non-coherent image datacomprising bright-field and/or dark-field and/or fluorescence signalsfrom the particle excited by the non-coherent light source.
 5. Thecytometer assembly of claim 4, wherein the analyzing module isconfigured to combine the diffraction image data and the non-coherentimage data.
 6. The cytometer assembly of claim 3, wherein the analyzingmodule is configured to classify the particles based on the coherencedistribution of the elastically scattered light.
 7. The cytometerassembly of claim 3, wherein the analyzing module is configured toextract a morphology feature of a structure of the particle based on thediffraction image data.
 8. The cytometer assembly of claim 7, whereinthe diffraction image data comprises image data from a defocusedposition with respect to the particle.
 9. The cytometer assembly ofclaim 7, wherein the structure of the particle comprises a volume andrefractive index of the cytoplasm and/or nucleus and/or mitochondrion ina biological cell.
 10. The cytometer assembly of claim 1, wherein thefluid controller is configured to form a laminar flow stream.
 11. Thecytometer assembly of claim 1, wherein the fluid controller comprises aflow cell having an index of refraction that is substantially similar toan index of refraction of the fluid sheath.
 12. The cytometer assemblyof claim 11, wherein the flow cell has at least one generally planarside.
 13. The cytometer assembly of claim 1, wherein the detector isconfigured to detect light scattered within an angle range centered atan angle offset from a direction of light propagation from the coherentlight source.
 14. The cytometer assembly of claim 13, wherein the angleis about 90 degrees.
 15. The cytometer assembly of claim 1, wherein theanalyzing module is configured to extract the three-dimensionalmorphology parameters based on a database of calculated and/orexperimentally determined cell images.
 16. A method of analyzingparticles in a flow cytometer to determine three-dimensional morphologyparameters, the method comprising: forming a hydrodynamically focusedflow stream, the stream including an outer sheath fluid and an innercore fluid; illuminating a particle in the inner core fluid with acoherent light source; detecting elastically scattered light from theparticle excited by the coherent light source; and extracting athree-dimensional morphology parameter of the particle from a spatiallycoherent distribution of the elastically scattered light.
 17. The methodof claim 16, wherein forming a hydrodynamically focused flow streamcomprises passing the flow stream through a fluid-filled gap in a fluidpassageway, and the spatially coherent distribution of elasticallyscattered light from the particle excited by the coherent light sourceis detected when the particle is in the gap.
 18. The method of claim 16,further comprising providing diffraction image data of the particlecomprising the spatially coherent distribution of the elasticallyscattered light resulting from excitation by the coherent light source.19. The method of claim 18, further comprising illuminating the particlewith a non-coherent light source and detecting non-coherent image datacomprising elastically scattered and/or fluorescence signals resultingfrom excitation by the non-coherent light source.
 20. The method ofclaim 19, further comprising combining the diffraction image data andthe non-coherent image data.
 21. The method of claim 18, furthercomprising classifying the particles based on the coherence distributionof the elastically scattered light.
 22. The method of claim 18, furthercomprising identifying a volume and refractive index of a structure ofthe particle based on the diffraction image data.
 23. The method ofclaim 22, wherein the structure of the particle comprises a volume andrefractive index of the cytoplasm and/or nucleus and/or mitochondrion ina biological cell.
 24. The method of claim 16, wherein forming ahydrodynamically focused flow stream comprises forming ahydrodynamically focused laminar flow stream.
 25. The method of claim16, further comprising providing a flow cell having an index ofrefraction that is substantially similar to an index of refraction ofthe sheath fluid.
 26. The method of claim 25, wherein the flow cell hasat least one generally planar side.
 27. The method of claim 16, whereinthe detected light is scattered within an angular range centered at anangle offset from a direction of light propagation from the coherentlight source.
 28. The method of claim 27, wherein the angle is about 90degrees.
 29. The method of claim 16, wherein the three-dimensionalmorphology parameter is extracted based on a database of calculatedand/or experimentally determined cell images.
 30. The method of claim16, wherein the detected light is detected from a defocused positionwith respect to the particle.
 31. A computer program product foranalyzing particles in a flow cytometer to determine three-dimensionalmorphology parameters, the flow cytometer having a hydrodynamicallyfocused flow stream including an outer sheath fluid and an inner corefluid, a coherent light source configured to illuminate a particle, anda detector for detecting a coherent distribution of elasticallyscattered light from the particle excited by the coherent light source,the computer program product comprising a computer usable storage mediumhaving computer-readable program code embodied in the medium, thecomputer-readable program code comprising: computer-readable programcode that is configured to receive diffraction image data comprising aspatially coherent distribution of elastically scattered light from theflow cytometer; and computer-readable program code that is configured toextract a three-dimensional morphology parameter of the particle fromthe spatially coherent distribution of the elastically scattered light.32. The computer program product of claim 31, further comprisingcomputer-readable program code that is configured to receivenon-coherent image data from the flow cytometer, the non-coherent imagedata comprising bright-field and/or dark-field image data of elasticallyscattered light signals and/or fluorescence image data from the particleresulting from excitation by the non-coherent light source.
 33. Thecomputer program product of claim 32, further comprisingcomputer-readable program code that is configured to combine thediffraction image data and the non-coherent image data for a particle inthe flow cytometer.
 32. The computer program product of claim 33,further comprising computer-readable program code that is configured toclassify the particles based on the coherence distribution of thescattered light.
 34. The computer program product of claim 31, furthercomprising computer-readable program code that is configured to identifya volume and refractive index of a structure of the particle based onthe diffraction image data.
 35. The computer program product of claim34, wherein the structure of the particle comprises a volume andrefractive index of the cytoplasm and/or nucleus and/or mitochondrion ina biological cell.
 36. The computer program product of claim 31, whereinthe three-dimensional morphology parameter is extracted based on adatabase of calculated and/or experimentally determined cell images.